Average Is Over - by Tyler Cowen

Workers more and more will come to be classified into two categories. The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? Worst of all, are you competing against the computer? Are computers helping people in China and India compete against you? If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other. That's why average is over.

In today's global economy here is what is scarce:

  1. Quality land and natural resources

  2. Intellectual property, or good ideas about what should be produced

  3. Quality labor with unique skills

Here is what is not scarce these days:

  1. Unskilled labor, as more countries join the global economy

  2. Money in the bank or held in government securities, which you can think of as simple capital, not attached to any special ownership rights (we know there is a lot of it because it has been earning zero or negative real rates of return)

I see marketing as the seminal sector for our future economy.

It sounds a little silly, but making high earners feel better in just about every part of their lives will be a major source of job growth in the future.

Let's draw up a simple list of some important characteristics in technologically advanced modern workplaces:

  1. Exactness of execution becomes more important relative to an accumulated mass of brute force.

  2. Consistent coordination over time is a significant advantage.

  3. Morale is extremely important to motivate production and cooperation.

Labor market polarization means that workers are, to an increasing degree, falling into two camps. They either do very well in labor markets or they don't do well at all. The longer-term trend is fewer jobs in middle-skill, white-collar clerical, administrative, and sales occupations. Demand is rising for low-pay, low-skill jobs, and it is rising for high-pay, high-skill jobs, including tech and managerial jobs, but pay is not rising for the jobs in between. The world is demanding more in the way of credentials, more in the way of ability, and it is passing along most of the higher rewards to a relatively small cognitive elite.

What Games Are Teaching Us

The way humans are playing chess with computers now is, I propose, a model that high earners will be emulating in years and decades to come.

As in chess, we can expect to see dramatic gains in the personal and professional lives of people who interpret machine feedback--of all kinds--quickly. A particular personality trait that doesn't come easily to everyone will be needed in a lot of situations: the ability to handle or maybe just ignore the ongoing appearance of stressful situations. For instance, if you're doing a business negotiation, some clever machine may be telling you to "walk away from the deal" a lot more often than you're used to. In the meantime, while you're waiting for them to call you back with a better offer, you will feel the pressure. Perhaps not everyone will wish to go down this computer-aided route, even if it promises more workplace and even dating success. Not everyone wants to go out on a date and have a buzzing iPhone in their pocket indicating "kiss her now" about an hour before such a move might usually be attempted. "Touch her on the shoulder, dummy" will be ignored by many of us. The gains in these cases will go to the hardy, those who can manage stress and embarrassment, but not necessarily to people who act like robots.

The computer is programmed to play for a win, not a draw. We can imagine competing intelligent-machine companies offering programs that seek out an active advantage in a typical human situation. No one rises to the top of the business world by breaking even on a lot of deals, and no one successfully woos a lot of women, or marries the right one, by acting "just okay" or neutral. People know that they need to take chances in complex situations, and they will buy tactical computer programs that help them do this. We're going to generate a lot of hairy, very complicated personal interactions, driven by real-time data analysis and computer intelligence. We'll use the computers to manage our risk-taking and seek out decisive advantages, just as it's increasingly done on chessboards.

Regular chess players who have gained the most from chess engines are those who understand how to train with the computer and how to learn from the computer. The advent of the programs rewards players who have very good memories and conscientious study habits; a player who can't remember his or her prepared lines won't benefit from this strategy. A human with a good memory can carry the learning of a computer program very effectively and apply that learning in a spontaneous fashion in, say, a job interview.

What are the broader lessons about the Freestyle approach to working or playing with intelligent machines? They are pretty similar to the broader lessons about labor markets from chapters two and three:

  1. Human–computer teams are the best teams.

  2. The person working the smart machine doesn't have to be expert in the task at hand.

  3. Below some critical level of skill, adding a man to the machine will make the team less effective than the machine working alone.

  4. Knowing one's own limits is more important than it used to be.

Decision Making

Human intuitions can misfire even when we study the world's best players, who've each been trained for decades to think rationally and compete for high stakes while drawing upon centuries of human experience with the game. The biggest problem is not outright gross blunders, but rather humans spending too much time thinking about the moves that "look good." It is precisely our reasoned, considered judgments that we should be more suspicious of. Despite all our bad moves, from chess to the game of love, the good news is that we can learn and reverse some of our excess reliance on intuition. As players we are becoming more like the computers. Top grandmasters are more likely than before to experiment with "ugly" moves--or at least to give them further study--because now they understand that ugly moves are more likely to work out.

What does all this mean for our decisions, especially in the workplace?

  1. Human strengths and weaknesses are surprisingly regular and predictable.

  2. Be skeptical of the elegant and intuitive theory.

  3. It's harder to get outside your own head than you think.

  4. Revel in messiness.

  5. We can learn.

The New World of Work

Just as labor market outcomes will move toward the poles of either "very good" or "very bad," so will the same be true for a lot of cities, states, geographic regions, and countries. Because of the internet and Amazon, among other developments, it is easier to become self-educated in many more different parts of the world. It is also easier to have a "good enough" or low budget (but happy) life in many more different parts of the world, again because of technology. But if you wish to be a high earner, learning from other well-educated people, geographic proximity is growing in importance, whether in companies or in leading amenities-rich cities or most likely in both.


Workable machine intelligence means that a good education no longer relies on living near a major city. Hanging out with the elites isn't as important as it used to be, unless you have some outlandish idea of the programs themselves as the new elites. The fact is that when humans and computers work together and cooperate, the rewards flow more readily to top talent, not to the socially well connected. Machine intelligence is the friend of the educational parvenu, albeit the disciplined, gutsy parvenu with high IQ.

It will become increasingly apparent how much of current education is driven by human weakness, namely the inability of most students to simply sit down and try to learn something on their own. It's a common claim that you can't replace professors with Nobel-quality YouTube lectures because the professor, and perhaps also the classroom setting, is required to motivate most of the students. Fair enough, but let's take this seriously. The professor is then a motivator first and foremost. Let's hire good motivators. Let's teach our professors how to motivate. Let's judge them on that basis. Let's treat professors more like athletics coaches, personal therapists, and preachers, because that is what they will evolve to be.

A New Social Contract?

The forces outlined in this book, especially for labor markets, will force a rewriting of the social contract, even if it is not explicitly recognized as such. We will move from a society based on the pretense that everyone is given an okay standard of living to a society in which people are expected to fend for themselves much more than they do now. I imagine a world where, say, 10 to 15 percent of the citizenry is extremely wealthy and has fantastically comfortable and stimulating lives, the equivalent of current-day millionaires, albeit with better health care. Much of the rest of the country will have stagnant or maybe even falling wages in dollar terms, but a lot more opportunities for cheap fun and also cheap education. Many of these people will live quite well, and those will be the people who have the discipline to benefit from all the free or near-free services modern technology has made available. Others will fall by the wayside. The measure of self-motivation in a young person will become the best way to predict upward mobility.